A novel end-to-end dropout concrete autoencoder for band selection on hyperspectral images outperforms prior methods on four scenes by training directly on the target band subset.
Optimal Clustering Framework for Hyperspectral Band Selec- tion,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
A novel end-to-end dropout concrete autoencoder for band selection on hyperspectral images outperforms prior methods on four scenes by training directly on the target band subset.